The present invention relates to a signal processing technique for separating signals from noise, and more specifically to a signal processing technique which separates signals from noise according to the magnitude of the fluctuation in the phase angles.
In underwater acoustic systems, receivers receive a great number of input signals in the form of electrical impulses corresponding to pressure variations in the media. Fluctuations in amplitude and phase are inherent in data measured in many environments, including the undersea acoustic environment. In many cases, fluctuations will degrade processor performance. Current techniques to minimize the effect of fluctuations in amplitude and phase include power averaging for durations that are much longer than the fluctuation periods as well as carefully designing the measurement apparatus to reduce the influence of fluctuation generation mechanisms.
Sonar system performance can be influenced by fluctuations in the amplitude of the received signal as well as by noise from environmental sources. For example, the presence of phase fluctuations can reduce the acoustic detection range of a submarine by making it more difficult to discriminate between signals from the submarine""s acoustic signature, clutter signals from ships, and the environmental noise. Current sonar system technology does not effectively discriminate between noise and signals, and fluctuations which degrade the signal processor""s performance are considered a nuisance to be avoided if possible, or to be ignored if they cannot be avoided.
Some of the natural causes of amplitude fluctuations in underwater acoustics which are considered to be the most important, for periods of a few minutes or less, are thermal and salinity finestructure, internal waves, turbulent particle velocities, ray-path or wave-front reflection from the sea surface, temporally variable source-receiver range separation, temporal changes in modes or ray-paths caused by source or receiver vertical motion, source radiation amplitude instability, and multipath arrivals.
Most signal processing technology used to differentiate between signals and noise and clutter concentrates on filtering higher power level inputs (assumed to be signals) from lower power level inputs (assumed to be noise). However, this approach is effective only if the signal has a greater amplitude than the surrounding environmental noise. Signals with amplitudes approximately the same or less those that of the environment are extremely difficult to distinguish from noise.
A different approach relies on the phenomenon that noise has amplitude but no real phase, because it is not the result of a wave with a sinusoidal shape. A Fast Fourier Transform method can be used to spectrum analyze these electrical signals, which results in a set of complex vectors, each containing a magnitude and phase angle. For noise, the phase that a signal processor detects is an artifact of the Fourier Transform (FT) process. Because noise has no true phase, the Fourier Transform process assigns a random phase angle to each amplitude value. Therefore, the phase angles of successive FT realizations of the noise, being random will appear as random phase fluctuations and will not be trackable as they will be for sinusoidal signals. Some signals (e.g., from submerged acoustic sources) will have small phase fluctuations between successive samples. Some signals (e.g., from ships) will have medium phase fluctuations, and noise will have large phase fluctuations. In some applications, such as detection of submerged acoustic sources, it is desirable to eliminate the signals with medium and large phase fluctuations and the noise, retaining only the small phase fluctuation signals. In other applications, such as detection of surface acoustic sources (e.g., surface ships), it is desirable to eliminate only the small phase fluctuation signals, and retaining only the medium and large phase fluctuation signals.
One feature of most currently available signal processors, which are not based on phase fluctuations, is that their governing equations are fixed, and do not adaptively change in response to changes in the environment. When environmental conditions change, the amplitude and phase characteristics of the ambient noise and clutter signals (e.g. acoustic signals from ships) also change. A processor with fixed signal processing parameters may be effective in one set of environmental conditions, but less effective in processing data in a different set of environmental conditions. A signal processor which would adapt to the signal and ambient fluctuation conditions would be extremely valuable for signal measurements which must be made over a time period in which the environment changes.
Another characteristic of the current approaches which do not discriminate between small phase fluctuation (SPF) signals, medium phase fluctuation (MPF) signals, and large phase fluctuation (LPF) signals is that an operator must monitor the results of the processor in order to evaluate whether a signal of interest is present. This is time intensive and expensive. An unattended automated system which could discriminate among SPF signals, MPF signals, LPF signals, and LPF noise is needed to provide inexpensive monitoring for underwater acoustic and other signals. This automatic detection capability would free an operator to examine only the signals meeting specific criteria, rather than monitoring all incoming signals to determine whether they were of interest.
One technique for increasing the signal to noise ratio of sinusoidal signals in random noise using a phase fluctuation approach is found in Wagstaff et al U.S. patent application Ser. No. 09/320,697, allowed, which is incorporated herein in its entirety. Another technique is described in U.S. Pat. No. 5,732,045, Fluctuations Based Digital Signal Processor Including Phase Variations issued to Wagstaff et al on Mar. 24, 1998, which is incorporated herein in its entirety.
Accordingly, an object of the invention is to vary processor parameters to change the degree of SPF signal attenuation, MPF signal attenuation, and LPF clutter signal attenuation, noise attenuation, signal gain, and other processor characteristics based on the magnitude of the phase fluctuations of the received signals and noise.
Another object of the invention is to adaptively modify the governing equations of a signal processor to more modify the degree of attenuation of signals and noise as the magnitude of their phase fluctuations changes.
Another object of the invention is to provide a computationally efficient method and apparatus to more severely attenuate clutter signals and noise as their phase fluctuations increase.
Another object of the invention is to utilize the phase fluctuations to increase the signal to noise ratios of small phase fluctuation signals.
Another object of the invention is to make a narrowband processor robust to temporal trends in the amplitude of the data, and to temporal fluctuations in the amplitude of the data.
Another object of the invention is to improve the spectral resolution of a narrowband processor.
Another object of the invention is to improve the spatial resolution of a narrowband processor.
Another object of the invention is to suppress the narrowband clutter due to signals that have large phase fluctuations.
Another object of the invention is to detect in an unalerted and automatic manner signals that have smaller phase fluctuations than clutter and noise.
Another object of the invention is to detect in an unalerted and automatic manner signals that have medium or large phase fluctuations.
Another object of the invention is to eliminate the ambient noise in a received signal.
Another object of the invention is to eliminate clutter.
Another object of the invention is to provide a signal processor with parameters that can be adjusted to distinguish between small phase fluctuation signals, clutter signals, and noise.
Another object of the invention is to enhance the performance of a signal processor for signals that have small phase fluctuations.
Another object of the invention is to enhance the performance of a signal processor for signals that have large phase fluctuations.
Another object of the invention is to enhance the performance of a signal processor for signals that have medium phase fluctuations.
In accordance with these, and other objects made apparent hereinafter, the invention concerns a method and apparatus which filters an N point time series of complex data, each datum having a vector amplitude r and a phase angle xcex8, the ith members of the time series of complex numbers being denoted ri and xcex8i, where i=1, 2, . . . , N. An estimate of the magnitude of the phase fluctuation "PHgr"i is determined for each ith sample. A term xcex93i, is calculated as xcex93i=F("PHgr"i), wherein F("PHgr"i) is a preselected function of "PHgr"i, and the term xcex3 is calculated as sum of xcex93i over the time series multiplied by a constant. A calculation of AWSUM ESP is made such that the each ri term is raised to the exponent xcex93i, and the rixcex93i terms are summed over the time series. The summation is then divided by a scaling factor, and the result is raised to the exponent xcex3.
In one embodiment of the invention, ASWUM ESP is calculated             AWSUM      ⁢              xe2x80x83            ⁢      ESP        =                  [                              1            SF2                    ⁢                                    ∑                              i                =                3                            N                        ⁢                          xe2x80x83                        ⁢                          r              i                              Γ                i                                                    ]            γ        ,
where SF2 is a scaling factor. This allows differentiation of small phase fluctuation signals from large phase fluctuation signals based on the value of AWSUM ESP, the AWSUM ESP value being large for signals with small phase fluctuation (SPF) and small for large phase fluctuation (LPF) signals, which are typically clutter signals and noise. If F("PHgr"i) is a quadratic equation in "PHgr"i, the value of AWSUM ESP could be large for MPF signals and small for SPF signals, LPF signals, and noise. Additionally, an automatic detection capability is based on calculation of ESP, which divides the incoherent average by the AWSUM ESP value. A small ESP will indicate a SPF signal, while a large ESP value will indicate a LPF signal. Other alternative methods can be used, such as the smallness of the standard deviation of the angles "PHgr", which is small for SPF signals, and large for LPF signals and noise.
These and other objects are further understood from the following detailed description of particular embodiments of the invention. It is understood, however, that the invention is capable of extended application beyond the precise details of these embodiments. Changes and modifications can be made to the embodiments that do not affect the spirit of the invention, nor exceed its scope, as expressed in the appended claims. The embodiments are described with particular reference to the accompanying drawings, wherein: